REMOVED OBJECT DEDECTION USING ONE CLASS CLASSIFICATION

Abstract

In this study, using surveillance videos, an operator interactive removed object detection algorithm is developed. Using dual foregrounds, static foreground regions are detected. Then, these detected regions are classified with a one-class classifier to decide whether a removed/stolen object is present in that region. For training the classifier, the region of interest is selected by the operator. Proposed algorithm further reduces the false alarms and detection delay with respect to the existing methods.